An improved teaching-learning-based optimization algorithm to solve job shop scheduling problems

Abstract

Job shop scheduling problem (JSP) is a strongly NP-hard combinatorial optimization problem. It is difficult to solve the problem to the optimum in a reasonable time. Teaching-learning-based optimization (TLBO) algorithm is a novel population oriented meta-heuristic algorithm. It has been proved that TLBO has a considerable potential when compared to the best-known heuristic algorithms for scheduling problems. In this paper, the traditional TLBO is improved to enhance diversification and intensification when exploring solutions for JSP. The improvements include changing the coding method, increasing number of teachers, introducing new learners and performing local search around potentially optimal solutions. To show effectiveness of the improved TLBO algorithm, the simulation results obtained by the improved TLBO for benchmark problems are compared with results obtained by the traditional TLBO and the best known lower bounds.